This node looks through a set of labeled examples to find an input that is closest to a query input. It then returns the target corresponding to that closest input. This provides an alternative to inferencing a trained neural network. ML Regression Proximity may be effective when the input dimension is low. ML Regression Proximity is a useful tool for troubleshooting and making sure that an ML setup is correct.

The first input of ML Regression Proximity should consist of labeled examples. For example, created using ML Example inside a SOP for loop or read in from a file using ML Example Import. The second input should be an input component that consists of the same contributions as each input component of the labeled examples. The input/output contributions that are specified by the multiparms should match the input/target contributions specified on the ML nodes from the origin labeled examples.

When ML Regression Train and ML Regression Inference is preferred, ML Regression Proximity can be useful for verifying that the data set was correctly generated. If you give an input component of a labeled example of the data set, ML Regression Proximity should return an exact match for the target. This assumes no two labeled examples in the data set have the same input component.